Why It’s So Hard to Predict Where the Pandemic Is Headed Next

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among professionals The danger facing Carl Bergstrom, a professor of biology at the University of Washington, is that he is often asked where the pandemic is heading. This question comes in many forms—what will next week be like, or the next school year, or next winter—and so on for as long as the virus is with us. But recently it has gained a special enthusiasm. Bergstrom works at the intersection of two relevant topics: how sentient beings like us act on information, and how biological phenomena like viruses spread. So if anyone has your answer man, that is.

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Lately, he’s been answering simply: “I don’t know.”

This is a short answer that hides a lot of fine nuance. Since the beginning of the pandemic, the job of disease modelers has not been to tell us where we are headed, but to prepare us for the many possible futures. This is a cumbersome business. Offering too many options in distress invites people to run away with one conclusion or another that suits them, leading to too many sacrifices or too much wishful thinking. (Remember when the Trump administration captured the most optimistic forecasts for declaring a pandemic by the end of summer– that is, The last summer?) Models can help policymakers decide where to put resources, and they can also help people like you and me find some moorings in an uncertain world. Oracles, however, they are not.


This is because at any point in a disease outbreak, a projection can rise or fall rapidly depending on its initial assumptions. It’s hard to make those assumptions. In the beginning, epidemiologists were scrambling to understand the basics of a new pathogen: how the virus spreads between people, how fast it incubates, super-spreaders and the asymptomatic in sowing so-called “invisible epidemics”. The role of infections. Over time, they got a better grip with the help of a full-court scientific press – more virological and immunological data about how the virus is transmitted, and more epidemiological data about what happens next. Once researchers understood how the virus moved, it became easier to determine how to back it with things like masks and social distancing.

But even with the answers, that uncertainty never goes away. Consider the present: Delta itself, of course, has also brought its own set of unknowns concerning its rapid replication and ability to infect. So is vaccination, including the extent to which the people who have been vaccinated have spread the virus, and how well the immunity lasts over time. All of these affect how severe the delta wave will be at a particular time and place. And, as we settle those questions, there’s always the possibility of a new version to kick off any long-term calculations. “We certainly have more information, but I wouldn’t say that the number of unknowns has really decreased,” says Emmanuela Gaquidou, a professor of health metrics science at the University of Washington. “I wouldn’t say we’re ever satisfied that we’ll have a model that will ever be used for more than a week.”

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Bergstrom suggests thinking of it this way: In March 2020, how would a disease modeler have predicted the ups and downs to come? The pandemic is now said to be in its fourth wave, but the term belies the far more complex topography of steep plateaus, gentle undulating hills and striking peaks. Even in retrospect, the pattern is hard to explain (and not just because time is hazy and no longer makes sense). Some changes were caused by the virus, and others because of the way we react. During the first wave, public life came to a halt following national stay-at-home orders. These were replaced by facade mandates and partial, sometimes halting, reopenings.

But it is also a scenario of turning despair and fatigue, the wild choice between pessimism and optimism, as last fall, when Americans returned to holiday travel in what was then the worst of the pandemic. And now, despite the peak of summer, which is as bad as ever, society is largely back to business as usual in many parts of the country. “People change their behavior dramatically during an ongoing pandemic,” says Bergstrom. “We constantly update our beliefs about how serious this is.”

In some ways, that means more experience with the pandemic could lead to more Uncertainty for modelers, no less. Beliefs and practices are now increasingly heterogeneous, varying from state to state and, in some cases, from city to city. Delta comes at a time when people are becoming more polarized in the wake of vaccination, and confused about what this means and how they should behave. “One month the mask mandate is fine, and the next month it is protest. It’s really hard to predict in advance,” Gakidou says.

Joshua Weitz, a professor who studies complex biological systems at the Georgia Institute of Technology, says, “The current topic that’s making things difficult now is disease states, how people react and how people react over time. ” It is a completely intuitive idea 18 months into the pandemic that our individual perceptions of risk, and the behaviors that result from it, must have a collective effect on the trajectory of the virus. But this was not a universal understanding in the beginning, notes Weitz, when some believed the pandemic would pass quickly. In modeling-speak, the term for it (a remnant of 19th-century epidemic theory) is Farr’s law: infections must peak and then subside at relatively uniform rates, producing a bell curve.

It was not going to obey the curve. Last spring, Weitz and the others could see that it was coming back for a second round. The first wave was not completely crushed, and many people remained susceptible. Cases peaked, then got stuck on the “shoulder” of the curve, declined at a slower rate than many estimated, and then stabilized at a higher rate of infection. The behavior, Weitz hypothesized, was not in sync with how the model predicted interventions such as the stay-at-home order. By studying mobility reports pulled from cell phone data, a proxy for how much social interaction people are experiencing, he was able to see that risky behavior decreased as fatalities climbed, but then turned corners. First started rebounding. “People look around, see the local situation, and they change their behavior,” Weitz says.

One consequence of these reactive behaviors is that it can be difficult to analyze how helpful supporting policies like mask and vaccine mandates are. There is a blur between cause and effect and between government actions and what the public is already doing as both react to rising and falling transmission rates. For example, he says, if you look at the timing of the mask mandate that was instituted last year in Georgia, and compare case rates before and after, you can determine that it has little effect. Was. But what if people realized that the case rate was rising and had already donned their masks? What if they start staying home now and then? Or what if it was the other way around: The requirement went into effect and few people followed the rules, so the masks didn’t get a chance to do their job? “There’s clearly a relationship there,” he says. “I can’t claim we got to the bottom of it.”

For modelers, this uncertainty presents a challenge. In order to evaluate when the delta boom may end, one can look at places where it has already risen, such as in the United Kingdom. But will it die quickly, or taper slowly, or perhaps plateau at a steady rate of infection? These scenarios, Weitz argues, will most likely depend on how people perceive and treat risk. The delta version would be expected to hit and eventually drop differently in high-vaccination Vermont It is in less vaccination than alabama. Different policies for schools and businesses will determine how much people from different groups will mix, and will be increased or decreased by the way people react independently.

“One of the big problems we’re facing right now is that people are so obsessed with numbers,” says Eli Sinclair, a doctoral student in psychology at Duke University. In A recently published study In Proceedings of the National Academy of Science, She asked participants how they perceived their local risk of infection, and found that it was difficult for most people to identify a potential risk, for example, if they gathered in groups of 100 versus 10 people, or were at home. The dangers of eating inside. Miscalibration goes in both directions, she notes. Despite their increased safety, vaccinators are acting more cautiously. Unrelated are, in general, no. “This disconnect between beliefs about risk and actual risk is probably going to get worse,” Sinclair says.

She adds that there was a glimmer of hope in her study, which is that the disconnect can be bridged. When people were shown data that was clearly related to local risk information, they tended to act in a more balanced way (usually, by taking less risk). This indicates that the models still have some function, Sinclair says, as long as they are presented in a way that is relevant to people’s real lives – showing how epidemics unfold locally and soon. may come.

It’s worth asking what we really want to know from these predictions, Bergstrom says. In the short term, depending on local practices and policies, it is helpful for each individual to have a model of how the delta wave will move in their area, as it can link them to risks and some understanding of how to behave. can provide. In Bergstrom’s case, this meant finding out how he felt about lecturing in front of a few hundred students when his college classes resumed later this month. Given the data, the mix of risks and precautions, and the need to be physically on campus, he felt he was ready. But was he ready to reinvent himself at his favorite bar, because…


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